Issue 15, 2022

Silica-templated photonic crystal sensors for specific detection of Cu2+

Abstract

Responsive photonic crystals have attracted extensive attention due to their features of transforming external stimuli into a variation of optical signals or structural colors. In recent years, the accumulation of heavy metal ions has become a serious threat to human health and the environment. Thus, a simple and rapid method for the accurate detection of metal ions is of great importance. Herein, an imidazole-based-silica inverse opal photonic crystal (IOPC) sensor is prepared. Three different particle sizes of SiO2 photonic crystals were used as templates for the preparation of an IOPC. The results show that the template presents a high specific surface area and interconnected nanopores. When the nanopores adsorb copper ions, the functional monomer imidazole will chelate with copper ions to form a flat quadrilateral structure. Then the nanopores of the IOPC shrink, which will result in the red shifting of the diffraction peak to complete the visual response sensing. When immersed in different concentrations of metal ions, the structural color of the IOPC changes, making it a visual sensor. In addition, it is proved that the imidazole-modified IOPC is specifically responsive to Cu2+, and the structural color of the sensor will shift from green to yellow after sensing. The detection limit is as low as 1 × 10−6 mol L−1, and the maximum offset of the diffraction peak can reach 50 nm. Therefore, the IOPC prepared here provides an ideal platform for the fast and high selective detection of Cu2+, and it has potential applications in the rapid detection of other heavy metal ions.

Graphical abstract: Silica-templated photonic crystal sensors for specific detection of Cu2+

Article information

Article type
Paper
Submitted
08 Apr 2022
Accepted
13 Jun 2022
First published
14 Jun 2022

Analyst, 2022,147, 3486-3493

Silica-templated photonic crystal sensors for specific detection of Cu2+

C. Chi, F. Bai, X. Xu, P. Qu, J. Xian, L. Li and D. Zhang, Analyst, 2022, 147, 3486 DOI: 10.1039/D2AN00619G

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